Cross-user dashboard behavior analysis and dashboard recommendations

ABSTRACT

Mechanisms are provided for performing cross-user dashboard behavior analysis and dashboard recommendation generation. Dashboard interfaces are presented to a user and the user inputs are tracked. Cognitive analysis of the user dashboard behavior pattern data is performed to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data. Cross-user correlation analysis operations are performed based on the user dashboard behavior pattern data and dashboard behavior pattern data of other users of a different user type to identify an intersection point. A recommendation output is generated and output that recommends at least one of a particular dashboard interface to access or a modification to the one or more dashboard interfaces to be performed. The recommendation is based on the identification of the intersection point and the determined reason for the user dashboard behavior.

BACKGROUND

The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for tracking dashboard usage and performing cross-user dashboard behavior analysis and dashboard recommendations.

In information technology, a dashboard is a user interface that organizes and presents information that is easy for users to view and interact with. The user interface is referred to as a “dashboard” since it resembles an automobile dashboard in that it typically has a variety of different information presented in different formats within the same overall visualization. However, a computing environment dashboard is more likely to be interactive than an automobile dashboard and provides user interface elements, such as virtual buttons, drop-down menus, and other generally known user interface mechanisms for receiving user input, through which the user can interact with the visualization in order to access detailed information.

Dashboard user interfaces have been utilized in various industries to present information to users in an interactive manner such that the user may drill down into the underlying analytics supporting the visualizations presented in the dashboard. Recently, dashboards have been utilized in the healthcare industry to present various levels of information to users within an organization. For example, Truven Health Analytics, an International Business Machines (IBM) Corporation company, has provided a variety of computer based solutions for providing health analytics and corresponding dashboards for presenting the resulting information to users.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described herein in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method, in a data processing system comprising at least one memory and at least one processor, is provided. The at least one memory comprises instructions that are executed by the at least one processor to configure the at least one processor to implement the method comprising presenting, by the data processing system, one or more dashboard interfaces to a user via a client computing device, and tracking, by the data processing system, user inputs to the client computing device at least during and after presentation of each of the one or more dashboard interfaces to the user via the client computing device to generate user dashboard behavior pattern data. The method further comprises performing, by behavior pattern reasoning logic of a cognitive computing system, cognitive analysis of the user dashboard behavior pattern data to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data. Moreover, the method comprises performing, by the behavior pattern reasoning logic of the cognitive computing system, cross-user correlation analysis operations based on the user dashboard behavior pattern data and dashboard behavior pattern data of one or more other users of a different user type to identify at least one intersection point where the user dashboard behavior of the user intersects with user dashboard behaviors of the one or more other users. In addition, the method comprises outputting, by the data processing system, a recommendation output that recommends at least one of a particular dashboard interface to access, or a modification to the one or more dashboard interfaces to be performed, to the user via the client computing device, based on the identification of the intersection points and the determined reason for the user dashboard behavior.

In other illustrative embodiments, a computer program product comprising a computer useable or readable medium having a computer readable program is provided. The computer readable program, when executed on a computing device, causes the computing device to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided. The system/apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

These and other features and advantages of the present invention will be described in, or will become apparent to those of ordinary skill in the art in view of, the following detailed description of the example embodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectives and advantages thereof, will best be understood by reference to the following detailed description of illustrative embodiments when read in conjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of an analytics system implanting a dashboard usage tracking and recommendation system in accordance with one illustrative embodiment;

FIG. 2 is a block diagram of an example data processing system in which aspects of the illustrative embodiments are implemented;

FIG. 3A is an example block diagram of the primary operational elements of a dashboard usage tracking and recommendation system in accordance with one illustrative embodiment;

FIG. 3B is an example diagram illustrating one example operation for identifying dashboards accessed by similar users for purposes of generating recommendations for a user in accordance with one illustrative embodiment;

FIG. 4 is an example diagram of a dashboard output with a dashboard recommendation portion in accordance with one illustrative embodiment;

FIG. 5 is a flowchart outlining an example operation for providing a dashboard recommendation to a user based on dashboard tracking and analysis in accordance with one illustrative embodiment;

FIG. 6 is a flowchart outlining an example operation for generating administrative consoles for providing dashboard recommendations across multiple users and/or organizations in accordance with one illustrative embodiment;

FIG. 7 is an example block diagram of an example embodiment of the present invention in which a cognitive analysis of cross-user dashboard behavior is performed; and

FIG. 8 is a flowchart outlining an example operation for performing cross-user dashboard behavior analysis and dashboard recommendations in accordance with one illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for tracking dashboard usage and generating recommendations as to dashboards or portions of dashboards that may be of interest based on analysis of such usage. In accordance with some illustrative embodiments, mechanisms are also provided for performing cognitive operations on tracked dashboard usage information across multiple types of users and/or user groups to thereby identify correlations and provide additional insights and recommendations for dashboards. The mechanisms of the illustrative embodiments track usage patterns of dashboards and predict the information that a user is attempting to obtain, correlates this information with dashboard configuration information specifying the information that various dashboards provide, and selects one or more dashboards that provide information corresponding to the predicted information for recommending to the user. In addition, the mechanisms of the illustrative embodiments analyze usage patterns across multiple users and/or organizations to determine the ways in which organizations and users within organizations are using various dashboards and generates recommendations with regard to the various dashboards.

In particular, it has been recognized that while dashboards provide a useful tool to present a variety of information to a user via one overall visualization of a general category of information, many times users must hunt for and utilize multiple different dashboards to obtain the particular information, or combination of information, that the user wishes to view. This stems from the fact that dashboards are pre-defined and dashboard creators, when generating a dashboard definition, must predict what information users are most likely going to want to view when requesting a particular dashboard, e.g., a dashboard creator that is generating a “Health Plan Dashboard” may determine that users are most likely going to want to view claims cost per member information, trend rates information, patient demographic information, high cost claimant (HCC) information, and the like.

However, many times users wish to see different combinations of information or utilize a dashboard expecting certain information to be present in the dashboard but which the creator did not predict the user would expect to be present in the dashboard. Thus, as a result, users often may not obtain the information that they are looking for from a single dashboard and may have to hunt for the information in other dashboards. In cases where users are not aware of the content of various dashboards or are not aware of what dashboards provide the information that they seek, the user may navigate many different dashboards and still not arrive at the desired information. This can cause frustration on the part of the user and may affect the efficiency or accuracy by which a user is able to perform assigned tasks. Moreover, organizations whose users are utilizing these dashboards or which are responsible for providing such dashboards may wish to know when users are not obtaining the information that they seek or otherwise know where improvements in the dashboard offerings may be achieved.

The illustrative embodiments provide mechanisms to track and analyze user interactions with dashboards, as well as their actions before and/or after interacting with the dashboard, e.g., searches performed via search engines, other dashboards accessed, instant messages sent, etc., to determine the information that a user is attempting to obtain through use of the dashboard and to determine whether the dashboard provided the information. Moreover, other usage tracking information, such as an amount of time spent accessing a dashboard, the portions of the dashboard interacted with by the user, and the like, may be tracked to give an indication of the usability of the dashboard and the type of information the user appears to be attempting to access. A pattern of usage, or dashboard user behavior, is generated based on the tracking and analysis and used to determine commonalities and differences between the dashboards accessed, the keywords searched, keywords included in instant messages, and the like. Based on this pattern of usage information, a determination is made, using predictive analytics, as to the information that the user is most likely attempting to access. Based on the determination of the information that the user is most likely attempting to access, the mechanisms of the illustrative embodiments perform a search of dashboard characteristic data to identify one or more dashboards, if any, that provide the information that the user appears to be searching for.

A recommendation may then be output to the user via a portion of a dashboard, a separate dialog box, or any other suitable output mechanism, that indicates what information the illustrative embodiments believe the user is looking for and the one or more dashboards that provide that information. This output may further provide links or other user interface elements by which the user may select the dashboard that he or she would like to access and thereby be redirected to the recommended dashboard. Moreover, in some illustrative embodiments, characteristics of other users that accessed the same dashboard, as well as their usage information regarding other dashboards, recommendations provided to these other users, and the like, may be leveraged to determine a recommendation as to other dashboards that the user may wish to access.

In addition, the mechanisms of the illustrative embodiments may log or otherwise store historical information about user usage patterns, as well as any recommended dashboards that the user actually selected based on the providing of the recommendation output to the user, for usage tracking and analysis across a plurality of users of the same and/or different organizations. For example, usage tracking and analysis information may identify commonalities between users that use a particular dashboard as to other dashboards accessed thereafter within a same user session, commonalities of search terms used in search queries after accessing the dashboard, commonalities of keywords included in instant messages sent after accessing a dashboard, or the like, which all point to users not obtaining the information they seek from the accessed dashboard and a commonality in the information that these users thought would be available in the dashboard. This provides insight into ways in which the accessed dashboard may be improved, a new dashboard that may be of use to users, or other modifications to the set of dashboards that are made available to users at the various organizations.

As mentioned above, the dashboard usage tracking and analysis may be done across organizations such that organizations whose users are attempting to access similar types of information from their dashboards, or are using similar dashboards in similar ways, may be identified and recommendations provided based on what other similar organizations' users are doing to access similar information or recommendations that have been provided to other similar organizations. In such cases, information about the various organizations may be utilized to identify similar organizations and identify similarities in usage patterns of their users regarding similar dashboards.

For example, a dashboard provider may provide the same or similar dashboards to multiple organizations. These organizations may customize these dashboards for their own personal use and may use them to access their own personal backend data stores. Tracking and analysis of usage patterns of users of the various organizations may provide insights into recommendations for how to modify, or add to, the dashboard offerings for a particular organization. In addition, looking at usage patterns across multiple similar organizations may identify areas where the dashboard offerings are not meeting the information needs of users in general, regardless of the particular organization, and provide insights into how all dashboard offerings to all organizations may be improved. Moreover, such across organization analysis may allow recommendations for one organization to influence recommendations provided to another organization based on similarities of usage patterns of their users and similarities of organization characteristics, e.g., organization size, geography, business segment, level of analytics utilized, etc.

Before beginning the discussion of the various aspects of the illustrative embodiments in more detail, it should first be appreciated that throughout this description the term “mechanism” will be used to refer to elements of the present invention that perform various operations, functions, and the like. A “mechanism,” as the term is used herein, may be an implementation of the functions or aspects of the illustrative embodiments in the form of an apparatus, a procedure, or a computer program product. In the case of a procedure, the procedure is implemented by one or more devices, apparatus, computers, data processing systems, or the like. In the case of a computer program product, the logic represented by computer code or instructions embodied in or on the computer program product is executed by one or more hardware devices in order to implement the functionality or perform the operations associated with the specific “mechanism.” Thus, the mechanisms described herein may be implemented as specialized hardware, software executing on general purpose hardware, software instructions stored on a medium such that the instructions are readily executable by specialized or general purpose hardware, a procedure or method for executing the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “at least one of”, and “one or more of” regarding particular features and elements of the illustrative embodiments. It should be appreciated that these terms and phrases are intended to state that there is at least one of the particular feature or element present in the particular illustrative embodiment, but that more than one can also be present. That is, these terms/phrases are not intended to limit the description or claims to a single feature/element being present or require that a plurality of such features/elements be present. To the contrary, these terms/phrases only require at least a single feature/element with the possibility of a plurality of such features/elements being within the scope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” if used herein with regard to describing embodiments and features of the invention, is not intended to be limiting of any particular implementation for accomplishing and/or performing the actions, steps, processes, etc., attributable to and/or performed by the engine. An engine may be, but is not limited to, software, hardware and/or firmware or any combination thereof that performs the specified functions including, but not limited to, any use of a general and/or specialized processor in combination with appropriate software loaded or stored in a machine readable memory and executed by the processor. Further, any name associated with a particular engine is, unless otherwise specified, for purposes of convenience of reference and not intended to be limiting to a specific implementation. Additionally, any functionality attributed to an engine may be equally performed by multiple engines, incorporated into and/or combined with the functionality of another engine of the same or different type, or distributed across one or more engines of various configurations.

In addition, it should be appreciated that the following description uses a plurality of various examples for various elements of the illustrative embodiments to further illustrate example implementations of the illustrative embodiments and to aid in the understanding of the mechanisms of the illustrative embodiments. These examples are intended to be non-limiting and are not exhaustive of the various possibilities for implementing the mechanisms of the illustrative embodiments. It will be apparent to those of ordinary skill in the art in view of the present description that there are many other alternative implementations for these various elements that may be utilized in addition to, or in replacement of, the examples provided herein without departing from the spirit and scope of the present invention.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

As noted above, the present invention provides mechanisms for tracking dashboard usage and generating recommendations as to dashboards or portions of dashboards that may be of interest based on analysis of such usage. It should be appreciated that the mechanisms of the illustrative embodiments may be utilized to track dashboard usage patterns and analyze such patterns to provide dashboard recommendations in many different domains. For example, one domain that will be referenced in the examples provided herein is the healthcare domain in which healthcare data are obtained from various sources, e.g., hospitals, doctor offices, health insurance providers, and the like, pre-processed to take the raw data and place them into a form that is useable by analytics engines, stored in a backend store, and upon which healthcare analytics are executed by the analytics engines to generate analytical and statistical data that is used as a basis for generating one or more dashboard representations. While this is an example implementation that will be used to provide examples herein, it should be appreciated that the present invention is not limited to such. Rather, the mechanisms of the illustrative embodiments may be utilized in any domain where dashboards are utilized by users to obtain information representations. This may include many different types of business, governmental, and other organization domains.

The illustrative embodiments may be utilized in many different types of data processing environments. In order to provide a context for the description of the specific elements and functionality of the illustrative embodiments, FIGS. 1-2 are provided hereafter as example environments in which aspects of the illustrative embodiments may be implemented. It should be appreciated that FIGS. 1-2 are only examples and are not intended to assert or imply any limitation regarding the environments in which aspects or embodiments of the present invention may be implemented. Many modifications to the depicted environments may be made without departing from the spirit and scope of the present invention.

FIG. 1 depicts a pictorial representation of an example distributed data processing system in which aspects of the illustrative embodiments may be implemented. Distributed data processing system 100 may include a network of computers in which aspects of the illustrative embodiments may be implemented. The distributed data processing system 100 contains at least one network 102, which is the medium used to provide communication links between various devices and computers connected together within distributed data processing system 100. The network 102 may include connections, such as wire, wireless communication links, or fiber optic cables.

In the depicted example, servers 104 and server 105 are connected to network 102 along with storage unit 106. In addition, clients 110 and 112 are also connected to network 102. These clients 110 and 112 may be, for example, personal computers, network computers, or the like. In the depicted example, servers 104 and 105 may provide data, such as boot files, operating system images, and applications, to the clients 110 and 112. Clients 110 and 112 are clients to servers 104 and 105 in the depicted example. Distributed data processing system 100 may include additional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is the Internet with network 102 representing a worldwide collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP) suite of protocols to communicate with one another. At the heart of the Internet is a backbone of high-speed data communication lines between major nodes or host computers, consisting of thousands of commercial, governmental, educational and other computer systems that route data and messages. Of course, the distributed data processing system 100 may also be implemented to include several different types of networks, such as for example, an intranet, a local area network (LAN), a wide area network (WAN), or the like. As stated above, FIG. 1 is intended as an example, not as an architectural limitation for different embodiments of the present invention, and therefore, the particular elements shown in FIG. 1 should not be considered limiting with regard to the environments in which the illustrative embodiments of the present invention may be implemented.

As shown in FIG. 1, one or more of the computing devices, e.g., server 105, may be specifically configured to implement an analytics system 100 having, as one component, a dashboard usage tracking and recommendation system 130 that operates as described hereafter in conjunction with other elements of the analytics system 100 to track and analyze dashboard usage and provide recommendations as to other dashboards a user may access to obtain the information they seek as well as provide recommendations as to improvements that may be made to the set of dashboard offerings provided. The configuring of the computing device, e.g., server 105, may comprise the providing of application specific hardware, firmware, or the like to facilitate the performance of the operations and generation of the outputs described herein with regard to the illustrative embodiments. The configuring of the computing device may also, or alternatively, comprise the providing of software applications stored in one or more storage devices and loaded into memory of a computing device, such as server 105, for causing one or more hardware processors of the computing device to execute the software applications that configure the processors to perform the operations and generate the outputs described herein with regard to the illustrative embodiments. Moreover, any combination of application specific hardware, firmware, software applications executed on hardware, or the like, may be used without departing from the spirit and scope of the illustrative embodiments.

It should be appreciated that once the computing device is configured in one of these ways, the computing device becomes a specialized computing device specifically configured to implement the mechanisms of the illustrative embodiments and is not a general purpose computing device. Moreover, as described hereafter, the implementation of the mechanisms of the illustrative embodiments improves the functionality of the computing device and provides a useful and concrete result that facilitates providing guidance to users as to dashboards where predictive analytics determine that the information sought by the user may be obtained as well as providing recommendations, such as to system administrators or other providers of dashboards, as to dashboard modifications or new dashboards that could be generated to provide information that the user and/or other users of the same or different organizations, tend to seek when accessing pre-defined existing dashboards.

As shown in FIG. 1, the server 105, which may represent a single or multiple server computing devices (e.g. a server farm or other collection of computing resources), is configured to provide an analytics system 100 that operates to receive data from data sources, such as servers 104 associated with one or more organizations, via network 102 and process those data to generate analytics and statistical data about a subject of interest which may then be output via one or more dashboards to users, such as via client computing devices 110 and 112. For example, healthcare related data may be obtained from servers 104 which may be associated with different healthcare provider organizations, e.g., a first server 104 may be associated with a hospital, a second server 104 may be associated with a pharmacy, a third server 104 may be associated with a doctor office, a fourth server 104 may be associated with a health insurance provider, etc. The healthcare data may include various types of data including, but not limited to, patient electronic medical records (EMR) data, health insurance claims data, pharmacy prescription fulfillment data, and the like. Any type of healthcare related data from any type of healthcare data source may be utilized with the mechanisms of the illustrative embodiments.

The data obtained from the various source systems 104, and even network attached storage 106, may be provided to the server 105 for processing by the analytics system 100. In some cases, pre-processing of the raw data obtained from these source systems 104 may be performed by the server 105 so as to place the data in a proper format for use by the analytics system 100, as well as standardize the data because of the differences in different systems 104, e.g., map medical codes to a common set of medical codes, etc. The pre-processed data may then be stored in backend data stores 150 for access by the analytics system 100 which performs analytics on the data with the results data also being able to be stored in the backend data stores 150 for use.

It should be appreciated that the term “analytics” as used herein refers to the use of mathematics, statistics, predictive modeling, and machine learning techniques to find meaningful patterns and knowledge in stored data. The “analytics” may be implemented through one or more analytics systems 110 which provide algorithms to process data stored in the backend data stores and generate analytics results data which represent the meaningful patterns and knowledge obtained from the pre-processed data obtained from the various source systems 104. The analytics may be applied within a single organization or across multiple organizations, and may combine analysis of data from one or a plurality of source systems 104.

The analytics systems 110 generate analytics results data which again, may be stored in the backend data stores 150, or in other storage (not shown) for utilization in providing output representations of these results data, e.g., graphical, textual, or audible outputs presenting the analytics results data for use by a user. The user experience systems 120 provide the logic for generating these output representations which, in accordance with the illustrative embodiments described herein, include one or more dashboards. A “dashboard” as the term is used herein refers to a graphical and/or textual user interface that provides a plurality of different representations of the same or different analytics results data in a single output, as previously discussed above. The particular dashboards that the user experience systems 120 may generate are pre-defined by dashboard creators and stored in a dashboard repository (not shown) associated with the user experience systems 120. The pre-defined dashboards are populated with specific data obtained from the analytics results data generated by the analytics systems 110 and stored in the backend data stores 150 or other storage, which is accessible via the backend data store interface system 140, and which may also provide the pre-processors and other logic for maintaining data in the backend data stores 150 and providing access to such stored data.

The dashboard usage tracking and recommendation system 130 provides the logic for tracking user interactions with the various dashboards that are provided by the user experience systems 120, as well as interactions the users make both before and after interacting with the dashboards, so as to generate a usage pattern for the user. The usage pattern is a representation of the types of actions performed by the user with the dashboard, lengths of time interacting with elements of the dashboard, and actions taken prior to, or subsequent to, interacting with the dashboard, such as with other user experience systems 120 including, but not limited to, search engines, instant messaging systems, and the like. The usage patterns are associated with dashboards provided by the user experience systems 120 and indicate the way that the user is using the dashboard and/or indications of other information that the user was not able to obtain from the dashboard.

For example, a user may wish to obtain average cancer treatment claims information for a particular geographical region. The user may request a “Cancer Claims” dashboard, but finds that the dashboard does not provide the particular cancer treatment claims information broken down by geographical region. The user may then access a different dashboard, entitled the “Claims by Geographical Region” dashboard, but which may not present claims information broken down into specific disease types. Thereafter, the user may perform a search through a search engine for “Cancer Claims by Region” and may instant message a colleague asking “Do you know where I can get information on cancer claims per region?” All of these actions may be recorded by the mechanisms of the illustrative embodiments for the user session such that these actions are tracked and may then be analyzed to identify a usage pattern for the user session that indicates that the user used the particular dashboards, appears to be looking for cancer claims information for geographical regions, and was not able to find that information in the specific dashboards accessed. Moreover, the mechanisms of the illustrative embodiments may record timestamp information for the actions to show how long the user viewed the various dashboards which may indicate the relative usefulness of the dashboards with regard to providing information similar to what the user was looking for, i.e. a user may view a dashboard for a longer period of time if the information is of use for their purposes than a dashboard that is clearly not useful.

Based on the usage patterns identified in the user session, the dashboard usage tracking and recommendation system 130, using predictive analytics based on the recorded action information, keywords in searches, keywords in instant messages, and any other indicators specific to the implementation, determines the information that the system 130 predicts the user is attempting to obtain. The indicator of the information determined from the predictive analytics, e.g., “Cancer Claims for a Geographical Region,” is compared to dashboard characteristics for the pre-defined dashboards to determine whether any pre-defined dashboard provides the information that the predictive analytics predict the user is attempting to access. The dashboard characteristics may be defined when the dashboard is created and may specify the types of information presented in the dashboard as well as the manner by which the information is presented, e.g., bar graph, pie chart, numerical representation, etc., and other dashboard characteristics that are pertinent to defining the type of dashboard and its content. If one or more dashboards exist that have dashboard characteristics that match the identifier of the information predicted to be sought by the user, then those dashboard(s) are selected as potential recommendations to be presented to the user.

The dashboard usage tracking and recommendation system 130 may then generate a recommendation output that is output to the user and includes an indication of the information that the system 130 predicts the user is looking for as well as the recommended dashboard(s) for providing the information. Moreover, the output may include hyperlinks, graphical user interface elements, or the like, for allowing the user to select or otherwise specify a desire to go to a recommended dashboard and have it provided to the user.

In addition to recommendations provided for individual users during user sessions with the analytics system 100, the dashboard usage tracking and recommendation system 130 may further provide usage tracking and analysis across multiple users in the same and/or different organizations and provide recommendations to system administrators and/or other dashboard creators as to the usage patterns observed across multiple users with regard to the pre-defined dashboards. For example, analytics may be executed on user interaction data tracked for each of the pre-defined dashboards to extract information about the way in which the users utilized the dashboards and the actions that they take both before and after interacting with the dashboards indicating the usefulness of the dashboard to the users' needs. For example, an analytic may indicate that users of dashboard A tend to go to dashboard B after viewing dashboard A, indicating that dashboard B may provide information that users were hoping to find in dashboard A. Moreover, user interactions with dashboard B may be recorded to see what portions of dashboard B the users interacted with after having gone to dashboard B from dashboard A, e.g., the user drills down into the underlying analytics data used to generate a particular portion of dashboard B.

As a result, differences between the information presented in dashboard B and dashboard A may be identified and a recommendation to modify dashboard A to include a link to dashboard B or to include portions of dashboard B in dashboard A may be generated. For example, the highest frequency of occurrences of interactions between a user and other dashboards, e.g., dashboard B, or other user experience systems 120, e.g., instant messaging system(s), search engine(s), etc., and/or portions thereof, that are associated with the dashboard being analyzed, e.g., dashboard A, may be utilized to generate recommendations as to additions or modifications to be made to the dashboard being analyzed. For example, based on the portions of another dashboard manipulated by user input subsequent to the dashboard being analyzed, e.g., dashboard A, and correlating information about those portions that indicates the type of analytics data represented in those portions as well as the way in which those analytics data are represented in the dashboard, a recommendation that similar types of analytics data should be included in the dashboard being analyzed and a recommendation as to the way in which those analytics data may be represented in the dashboard may be provided. In some cases, the actual portion of code used to represent the portion of the other dashboard may be provided as an example portion of code to include in the dashboard code for the dashboard being analyzed, e.g., dashboard A.

Similarly, analyzing the frequency of occurrence of search terms searched by users prior to or following the user's interactions with the dashboard being analyzed may identify terms representing analytics data that the users are attempting to gain access to and may be a basis for correlating with the metadata indicating the types of analytics data represented in other dashboards. A similar approach may be performed using natural language processing of instant messages and other types of communications conducted by users via the user experience systems 120 to thereby identify terms and/or phrases that users use most frequently in combination with their interactions with a particular dashboard being analyzed. These terms/phrases may be used to correlate with metadata or other information describing the types of analytics data represented in the various dashboards. From this correlation, recommendations as to links to other dashboards and/or portions of other dashboards that may be included in the dashboard being analyzed may be generated and output to a dashboard creator and/or provider of dashboards as part of the analytics system 100.

It should be appreciated that these are only examples of recommendations that may be generated using the mechanisms of the illustrative embodiments. The actual coding and/or inclusion of recommendations into the dashboards may be performed using known or later developed dashboard authoring tools. Other types of recommendations may be generated using the mechanisms of the illustrative embodiments without departing from the spirit and scope of the present invention.

As noted above, the mechanisms of the illustrative embodiments utilize specifically configured computing devices, or data processing systems, to perform the operations for tracking dashboard usage patterns and providing various levels of dashboard recommendations to the user and/or system administrator or other dashboard provider. These computing devices, or data processing systems, may comprise various hardware elements which are specifically configured, either through hardware configuration, software configuration, or a combination of hardware and software configuration, to implement one or more of the systems/subsystems described herein. FIG. 2 is a block diagram of just one example data processing system in which aspects of the illustrative embodiments may be implemented. Data processing system 200 is an example of a computer, such as server 104 in FIG. 1, in which computer usable code or instructions implementing the processes and aspects of the illustrative embodiments of the present invention may be located and/or executed so as to achieve the operation, output, and external affects of the illustrative embodiments as described herein.

In the depicted example, data processing system 200 employs a hub architecture including north bridge and memory controller hub (NB/MCH) 202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204. Processing unit 206, main memory 208, and graphics processor 210 are connected to NB/MCH 202. Graphics processor 210 may be connected to NB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connects to SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive 230, universal serial bus (USB) ports and other communication ports 232, and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus 240. PCI/PCIe devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM 224 may be, for example, a flash basic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD 226 and CD-ROM drive 230 may use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating system coordinates and provides control of various components within the data processing system 200 in FIG. 2. As a client, the operating system may be a commercially available operating system such as Microsoft® Windows 7®). An object-oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provides calls to the operating system from Java™ programs or applications executing on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM eServer™ System P®) computer system, Power™ processor based computer system, or the like, running the Advanced Interactive Executive (AIX®) operating system or the LINUX® operating system. Data processing system 200 may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit 206. Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as HDD 226, and may be loaded into main memory 208 for execution by processing unit 206. The processes for illustrative embodiments of the present invention may be performed by processing unit 206 using computer usable program code, which may be located in a memory such as, for example, main memory 208, ROM 224, or in one or more peripheral devices 226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may comprise one or more buses. Of course, the bus system may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit, such as modem 222 or network adapter 212 of FIG. 2, may include one or more devices used to transmit and receive data. A memory may be, for example, main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG. 2.

As mentioned above, in some illustrative embodiments the mechanisms of the illustrative embodiments may be implemented as application specific hardware, firmware, or the like, application software stored in a storage device, such as HDD 226 and loaded into memory, such as main memory 208, for execution by one or more hardware processors, such as processing unit 206, or the like. As such, the computing device shown in FIG. 2 becomes specifically configured to implement the mechanisms of the illustrative embodiments and specifically configured to perform the operations and generate the outputs described herein with regard to one or more illustrative embodiments directed to the tracking of dashboard usage data for users and providing dashboard recommendations based on analysis of the tracked dashboard usage data and actions of users surrounding their interactions with the dashboards.

Those of ordinary skill in the art will appreciate that the hardware in FIGS. 1 and 2 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in FIGS. 1 and 2. Also, the processes of the illustrative embodiments may be applied to a multiprocessor data processing system, other than the SMP system mentioned previously, without departing from the spirit and scope of the present invention.

Moreover, the data processing system 200 may take the form of any of a number of different data processing systems including client computing devices, server computing devices, a tablet computer, laptop computer, telephone or other communication device, a personal digital assistant (PDA), or the like. In some illustrative examples, data processing system 200 may be a portable computing device that is configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data, for example. Essentially, data processing system 200 may be any known or later developed data processing system without architectural limitation.

FIG. 3A is an example block diagram of the primary operational elements of a dashboard usage tracking and recommendation system in accordance with one illustrative embodiment. The example shown in FIG. 3 is an example of an implementation of one illustrative embodiment that operates on healthcare data obtained from a plurality of healthcare data systems 310, e.g., doctor office computer systems, hospital computer systems, pharmacy computer systems, health insurance computer systems, government healthcare agency computer systems, or the like. It should be appreciated that this is only one illustrative embodiment and one example of a domain in which the mechanisms of the illustrative embodiments may be implemented. Any domain in which dashboards are utilized to represent analytics data generated from the application of analytics to raw data obtained from one or more source systems may be used with the mechanisms of the illustrative embodiments without departing from the spirit and scope of the present invention.

As shown in FIG. 3A, raw healthcare data from systems 310 are received by the analytics system 300 and optionally subjected to data pre-processing system(s) 312 to pre-process the raw data and generate pre-processed data that is stored in the backend data stores 320. It should be appreciated that in some implementations pre-processing may not be necessary and instead the raw data may in fact be stored in the backend data stores 320. The raw data may be received from the healthcare data systems 310 via one or more data networks, for example, with the pre-processing systems 312 performing operations to standardize or otherwise re-format the data for use by the analytics system 300 since the various healthcare data system(s) 310 may each utilize their own desired formats, references (e.g., medical codes, etc.), and other types of data that may need to be mapped to a common or standardized format that correlates information from the various healthcare data system(s) 310. The raw data may represent any type of healthcare related data that are pertinent to the particular implementation, such as electronic medical records (EMRs) of patients, admissions information from hospitals, insurance claims information, prescription information from pharmacy systems, instance information for particular disease reports from a governmental agency, or the like.

The pre-processed data stored in the backend data stores 320, and/or raw data depending on the particular implementation, may be provided to analytics engines 330 for the performance of analytics operations to generate analytics results data that are stored in the backend data stores 320 and/or provided to the user experience system 340. The analytics engines 330 may perform various analytical analyses on the pre-processed and/or raw data to generate meaningful patterns and knowledge. For example, the raw or pre-processed healthcare data may be analyzed by analytics engines 330 to generate such values as claims costs per member per time period, trend rate information, various types of demographics information analytics, high cost claimant information, or the like, examples of which are shown in FIG. 4 discussed hereafter.

The analytics data generated by the analytics engine(s) 330 may be provided, either from the backend data stores 320 or directly from the analytics engines 330, to the user experience system 340 which comprises logic for generating one or more dashboards 360 based on predefined dashboard data structures in predefined dashboards storage 345. The predefined dashboard data structures may specify templates, code, or other mechanisms for defining the dashboards including the portions of the dashboards, the analytics data used to generate the portions of the dashboards, and the like. The user experience system 340 may, based on a request from a user via a client system 370 or administrator console 380, generate a corresponding dashboard that matches the request by retrieving the appropriate predefined dashboard data structure from the storage 345 and populating the portions of the dashboard 360 with analytics data generated by the analytics engines 330. The request may specify criteria for the analytics data to use when generating the dashboard 360, e.g., a time frame, patient characteristics data, geographic region, etc. which may thereby be used to filter the analytics data represented in the dashboard 360.

The generated dashboards 360 are provided to the client systems 370 and user interactions with the dashboards 360 may be monitored by the dashboard usage tracking and recommendation (DUTAR) system 350. While the DUTAR system 350 is shown as being part of the user experience system 340, the DUTAR system 350 may also be separate from the user experience system 340 yet part of the analytics system 300.

The DUTAR system 350 provides logic for listening to the user inputs to dashboards and/or other user experience systems 365, such as search engines, instant messaging systems, and the like, to log and/or record user inputs both before, during, and after interacting with a dashboard of interest. The DUTAR system 350 may utilize agents 375, i.e. portions of code designed to log and transmit information about user interactions with user experience systems, deployed on client systems to obtain the user input information tracking the user's input patterns. In this way, the user's input patterns may be identified before, during, and after a dashboard is provided to the user which may then be indicative of the type of data the user is attempting to access. This information may be utilized by predictive analytics logic 357 of the DUTAR system 350 to predict what type of data or information the user is attempting to access and generate a recommendation for the user. Moreover, the input patterns of the user may be accumulated with other input pattern data for the particular dashboard and stored in the dashboard usage data storage 355 for presentation of dashboard usage metrics information and/or recommendations as to how to improve dashboard offerings via administrator consoles 380.

The recommendations for the user during a user session with a dashboard 360 may be generated based on the predictive analytics of predictive analytics logic 357 in the DUTAR system 350 and may be presented to the user via his or her client system 370 either as a separate recommendation or as a recommendation integrated with a presented dashboard 360. The recommendation, as noted above, may be generated by comparing the identifier of the predicted data or information that the predictive analytics logic 357 of the DUTAR system 350 determines the user is looking for, to identifiers of data or information presented in various ones of the predefined dashboards 345 based on the metadata associated with the various predefined dashboard data structures in the storage 345. Those dashboards data structures having metadata that have identifiers matching that of the predicted data or information may be returned, as part of search results generated from searching the predefined dashboards 345 based on the predicted data or information identifier(s), to the DUTAR system 350 and a corresponding recommendation recommending those returned dashboards is generated and sent to the client system 370. As noted above, this recommendation may be in the form of a suggestion with corresponding links or other mechanisms for allowing a user to access the recommended dashboards from the recommendation output.

The dashboard usage data 355 store cumulative usage metric information for each predefined dashboard in the storage 345 indicating various characteristics of user inputs associated with the predefined dashboard which may also be correlated with particular user characteristics information stored in the user registry 356, such as may be part of a user profile data structure or the like. For example, the metrics information may indicate the number of users that have input certain search terms into search engines before or after using the dashboard, metrics of how many times users accessed portions of the particular dashboard, metrics of how many times users accessed each of one or more other pre-defined dashboards either before or after accessing the dashboard of interest, metrics of the number of times users used certain terms or phrases during instant messaging within a predefined time period before or after accessing the dashboard of interest, or the like. These metrics may be presented to an administrator via one or more administrator consoles 380.

Moreover, the DUTAR system 350 may also analyze these metrics and present recommendations to the administrator as to modifications to the dashboard of interest. For example, as noted above, highest occurrences of particular terms or phrases may be matched to terms/phrases in metadata descriptions of pre-defined dashboards in the storage 345 to determine which pre-defined dashboard data structures present information related to those terms/phrases. Similarly, based on the determination of portions of other dashboards, or dashboards themselves, accessed by users more often than other dashboards or portions thereof, recommendations as to modifications to the dashboard of interest to include a link to or portions of the other dashboard may be generated. All of this information may be presented to an administrator so that he can determine what modifications, if any, to perform to a dashboard so as to improve the dashboard offerings provided in the predefined dashboards storage 345.

In some illustrative embodiments the DUTAR system 350, e.g., the predictive analytics logic 357 of the DUTAR system 350, may predict dashboards that may be of interest to a particular user based on clustering of similar users based on the user registry 356 as well as information in the dashboard usage data 355 indicating which users access the same dashboard. For example, for a user that has accessed dashboard A, other users that have accessed dashboard A may be retrieved from the dashboard usage data 355 which may log identifiers of users that have accessed each of the dashboards over a predetermined period of time. Based on the identification of other users that have accessed the same dashboard as the present user, i.e. dashboard A, corresponding user characteristic information from user registry 356 may be obtained. Users with similar characteristics may be clustered together and the dashboard usage data 355 for the other users in the same cluster as the present user may be accessed to determine what other dashboards the other users accessed which the present user has not accessed yet. These other dashboards may be identified as dashboards that may be recommended to the present user for potentially providing the information or data sought by the present user.

An example of this operation is represented in FIG. 3B. As shown in FIG. 3B, the example operation for identifying dashboards accessed by similar users for purposes of generating recommendations depicted models a single user's behavior and compares this behavior to other users with similar characteristics to form a recommendation based on like users. For example, as shown in FIG. 3B, two sets of dashboards accessed by the present user 390 and other users 392 are conceptually represented with an overlap region 393 representing dashboards that both the present user 390 and the other users 392 have accessed while the other non-overlap regions represent dashboards not accessed by both the present user 390 and the other users 392.

A portion 395 of the dashboards accessed by the other users 392 may be selected for use in generating recommendations to the present user. This portion 395 may be selected in a variety of different ways based on the dashboard usage data 355. For example, the portion 395 may be selected based on the set of dashboards in the other users 392 set that a predetermined number of the other users have accessed, e.g., if dashboard B is accessed by 30% or more of the other users 392, then dashboard B may be selected as a recommendation. In other illustrative embodiments, those dashboards that were accessed within the same session as dashboard A may be selected as recommendations. Any suitable criteria for selecting dashboards accessed by other similar users to the present user, which have not been accessed by the present user yet during the present session, may be used without departing from the spirit and scope of the present invention. Of course, other methodologies and algorithms for selecting a sub-set of the dashboards in the similar users 392 set may be used without departing from the spirit and scope of the present invention.

As touched upon above, in some illustrative embodiments, recommendations may be constructed based on the user's behavior as it relates to his or her interactions with other user experience systems and, in some cases, the subject matter of the content that the user accessed via these other user experience systems. For example, a recommendation may be constructed based on a user's behavior as it relates to his or her consumption of whitepapers versus time spent on specific dashboards. If a user has read multiple whitepapers related to prescription medication cost drivers, for example, and has spent a significant amount of time interacting with a prescription medication cost dashboard, as may be indicated by the dashboard usage data 355 in comparison to one or more threshold values defining a “significant” amount of time, the DUTAR system 350 may recommend other dashboards tied to the cost of prescription medications.

For example, assume that a user spends time reading whitepapers, via another user experience system 365, related to prescription drug costs for the drug Embrel® and interacting with a dashboard designed to show how much the user's organization is currently spending on prescription medication. A recommendation would then occur for dashboards tied to the drug Embrel® and/or dashboards tied to the cost effective treatment of rheumatoid arthritis, i.e. a medical condition that is treated with the drug Embrel®.

Of course, combination approaches may also be applied when generating recommendations, where these combination approaches combine similar user dashboard usage information with user behavior information to generate recommendations for a user. Moreover, recommendations made for one user may also be logged in the dashboard usage data 355 and/or as part of a user's profile in the user registry 365, and used as a basis for generating recommendations for other users having similar characteristics or similar dashboard usage behavior patterns. For example, a rheumatoid arthritis dashboard may be recommended to the present user even though the user has not exhibited the above behavior of reading white papers focused on Embrel®, but who does instead have enough similarities with other users for which a rheumatoid arthritis dashboard was recommended, such as by using one of the implementations above.

These are but examples of ways in which the logic of the DUTAR system 350 may generate recommendations for users based on the tracked information in the dashboard usage data 355 and/or the user profile information in the user registry 356. Other ways of implementing logic for generating recommendations based on analysis of the tracked information may occur to those of ordinary skill in the art in view of the present description. These other ways of implementing such logic are intended to be within the spirit and scope of the present invention and the present description.

Thus, the illustrative embodiments provide mechanisms for tracking user usage of dashboards and providing recommendations as to where the users can obtain information that the users appear to be attempting to access. These mechanisms utilized predictive analytics to determine what information or data the user is attempting to access based on the pattern of inputs the user provides before, during, and/or after the presentation of a dashboard. Based on the prediction, metadata associated with pre-defined dashboards may be searched to identify other dashboards that provide the predicted data or information being sought by the user. In addition, dashboard usage metrics may be accumulated across multiple users for each dashboard and used to present information to an administrator as well as may be used as a basis for generating recommendations to an administrator as to ways to improve the dashboard offerings in the predefined dashboards storage 345.

FIG. 4 is an example diagram of a dashboard output with a dashboard recommendation portion in accordance with one illustrative embodiment. As shown in FIG. 4, the dashboard 400 includes a plurality of portions 410-440 for presenting different types of analytics results data or presenting the analytics results data in different ways. The presentation of these analytics results data may take many different forms, including graphs, charts, numerical values, text, and the like, with each individual portion 410-440 of the dashboard potentially presenting corresponding analytics results data in different ways or in different formats. For example, portion 410 in FIG. 4 presents a line graph to represent claims costs per member. Portion 420 presents a bar graph of trend rates. Portion 430 provides a chart with demographics information and portion 440 presents a chart with high cost claimant information.

In accordance with the mechanisms of the illustrative embodiments, based on the operation of the dashboard usage tracking and recommendation system 130, 350 of the analytics system 100, 300 described above, the dashboard 400 includes a portion 450 for outputting dashboard recommendations for the user. While this portion 450 is shown as part of a dashboard 400, it should be appreciated that the portion 450 may in fact be separate from any dashboard, such as in the case of a pop-up window, instant message, or any other mechanism by which to present an output of a dashboard recommendation to a user.

As shown in FIG. 4, the portion 450 includes an identifier of the information that the dashboard usage tracking and recommendation system 130, 350 predicts, via its predictive analytics on the dashboard tracking data, the user is attempting to access, e.g., in the depicted example, the system 130, 350 predicts that the user is attempting to access information regarding “Net Costs for Cancer Patient Screenings” and outputs a message asking the user if they are looking for this information. The portion 450 further includes a listing of recommended dashboards that provide the information that the system 130, 350 predicts the user is attempting to access. This listing may include hyperlinks to the dashboards or other user interface elements for allowing the user to select a corresponding entry in the listing so as to immediately access the recommended dashboard.

It should be appreciated that the depiction in FIG. 4 is only an example. Many modifications may be made to the depiction in FIG. 4 without departing from the spirit and scope of the present invention.

FIG. 5 is a flowchart outlining an example operation for providing a dashboard recommendation to a user based on dashboard tracking and analysis in accordance with one illustrative embodiment. The operation outlined in FIG. 5 may be implemented, for example, by a dashboard usage tracking and recommendation system 130, 350 described above with regard to FIGS. 1 and 3. As such, the system 130, 350 may operate in conjunction with other logic and systems to provide input data upon which the system 130, 350 operates and with which the system 130, 350 communicates to provide outputs and receive inputs.

As shown in FIG. 5, the operation starts with the receipt of a consumer (or user) request for dashboard(s) during a current session (step 510). Instance(s) of the dashboard(s) are generated using the particular consumer's data (step 520) and the user interactions with the dashboard(s) as well as actions taken before and after interacting with the dashboard(s) are tracked for the session, which are collectively referred to as tracked user interactions (step 530). Analytics are applied to the tracked interaction data to identify one or more usage patterns (step 540). Based on the usage patterns, a prediction of the types of data the user is attempting to access is generated (step 550).

Based on the predicted types of data the user is attempting to access, pre-defined dashboards that provide the predicted types of data, or portions of pre-defined dashboards that provide the predicted types of data, are identified, such as via matching with dashboard characteristics information associated with the pre-defined dashboards (step 560). Dashboard recommendations are then generated based on the correlation of the predicted types of data being sought by the user with the dashboard characteristics of the various dashboards or portions thereof (step 570). The recommendations are then output to the user via a user interface, which in one implementation may be a currently viewed dashboard, or a pop-up window, or other output message mechanism (step 580). The operation may then receive user input to the recommendation output and provide a user selected recommended dashboard in response to the user selection to go to the recommended dashboard (step 590). The operation may then terminate.

FIG. 6 is a flowchart outlining an example operation for generating administrative consoles for providing dashboard recommendations across multiple users and/or organizations in accordance with one illustrative embodiment. Again, the operation outlined in FIG. 6 may be implemented, for example, by a dashboard usage tracking and recommendation system 130, 350 described above with regard to FIGS. 1 and 3. As such, the system 130, 350 may operate in conjunction with other logic and systems to provide input data upon which the system 130, 350 operates and with which the system 130, 350 communicates to provide outputs and receive inputs.

As shown in FIG. 6, the operation starts by retrieving dashboard usage tracking and/or recommendation information across a plurality of consumers (or users) associated with the same or different organizations (step 610). The usage tracking and/or recommendation information is analyzed to identify usage patterns across consumers (step 620). The usage patterns identified are correlated with dashboards or portions of dashboards (step 630) and recommendations are generated for modifying dashboards or generating new dashboards to address the usage patterns (step 640). The recommendations are then used as a basis for generating and outputting administrative consoles with the dashboard recommendations, usage pattern data, and results of the analysis/correlation of the usage pattern data with dashboards or portions thereof (step 650). The operation then terminates.

Thus, the illustrative embodiments provide mechanisms for tracking usage of dashboards and, via predictive analytics, identify types of information that users are attempting to access via the dashboards and providing them with recommendations as to other dashboards that may provide the information that they seek. The illustrative embodiments further provide mechanisms for analyzing usage patterns of users across one or more organizations to identify improvements that may be made in the dashboard offerings of these organizations. In this way, users are provided with recommendations as to where they may find the information that they appear to be looking for and thereby reduce frustration due to an inability to find the information they seek. Moreover, organizations are presented with recommendations as to how to improve upon the representations of analytics data via dashboards so as to reduce frustration and improve productivity of the users in their organizations at a larger scale.

In some illustrative embodiments, either in addition to, or in replacement of, the predictive analytics logic of the DUTAR system, a cognitive computing system may be employed to not only analyze the resulting analytics of the analytics engines and/or raw data, as well as patterns of behavior of the users to generate dashboard recommendations, but also to evaluate the reasons behind such user behavior, across different types of users, and determine appropriate recommendations based on these determined reasons. Moreover, the cognitive system may operate to determine ways in which the dashboard usage behaviors of different users may be combined based on the identified reasons for such usage behaviors and how these reasons may intersect with one another. In this way, dashboard offerings may be generated, recommendations for dashboard improvements may be generated, and the like, such that dashboards may be offered that satisfy the needs of different users across different user groups.

FIG. 7 is an example block diagram of a DUTAR system in accordance with one illustrative embodiment. The DUTAR system 700 in FIG. 7 may be similar to the DUTAR system 350 in FIG. 3 but with modifications made to incorporate a cognitive system 710 and interfaces to, or inclusion directly of, supportive systems and data sources, such as dashboard knowledge base 720, organizational database 730, and the like, into the DUTAR system 700. Elements shown in FIG. 7 having similar reference numbers to elements in FIG. 3 operate in a similar manner. The following discussion will be focused primarily on the additions made in this illustrative embodiment with regard to the implementation of a DUTAR system 700 with a cognitive system 710 that provides behavior pattern reasoning logic 712 to perform cognitive analysis of user behavior patterns across different groups of users to facilitate the generation of dashboards and/or dashboard recommendations.

As noted above, the predictive analytics logic 357 may be utilized to analyze the analytics results data, raw data, and/or storage analytics results data from backend data stores 320 and/or analytics engines 330 (not shown in FIG. 7), to predict what information a user may be looking for and based on such a prediction, provide a dashboard, or dashboard recommendation referencing a specific dashboard, that provides the predicted information that the user may be looking for. Moreover, dashboard metrics and recommendation as to new or modified dashboards that may be provided may be sent to administrator consoles 380 to recommend expanding or modifying the dashboard offerings for particular types of users. In some illustrative embodiments, analysis of user registry 356 information may be employed to correlate behaviors of similar users as noted above.

While this provides useful insights for providing recommendations for dashboards on an individual user or user group basis, additional insights may be obtained when evaluating cross-user group behaviors and correlating such behaviors to provide recommendations as to dashboards that are useful to a plurality of different types of users and/or user groups. However, relying only on predictive analytics may not provide such insights and a more cognitive approach may be useful.

The cognitive system 710 of the depicted illustrative embodiment in FIG. 7 may augment such predictive analytics based operations by further including a cognitive analysis of user behaviors across user groups to identify correlations between information needs and dashboard usage of users from these different user groups. While an embodiment will be described in which both predictive analysis and cognitive analysis are performed, it should be appreciated that the cognitive analysis performed by the cognitive system 710 may be implemented directly on the raw data/stored analytics results data or other analytics results data from analytics engines without having to perform predictive analysis.

To illustrate the need for such a mechanism, consider a healthcare dashboard implementation of the user experience system 340 in which various dashboards are predefined in the predefined dashboards database 345. Various users may use different ones of those dashboards, and may in fact use different portions of the same dashboards in different ways and for different reasons. For example, a clinician may demonstrate a usage behavior in which the clinician looks at dashboards providing information regarding specific drugs or classes of drugs and their usage in treating particular diseases in particular groups of patients, and may in fact routinely avoid certain dashboards or ignore portions of dashboards. This information tends to be relatively more volatile. An administrator may demonstrate a behavior of looking at dashboards that provide information regarding costs per class of drugs, treatment costs per disease, outbreaks of diseases in a particular geographical region, or other more high level stable analytics and metrics. These behaviors may intersect at intersection points in that the clinician may look to dashboards providing information about specific classes of drugs for determining applicability to patient diseases whereas the administrator may look at dashboards directed to these classes of drugs for purposes of cost analysis. The administrator may see that a certain lower costs drug or class of drugs is not being prescribed by clinicians and not know the reason why from the dashboards the administrator accesses. However, through analysis of the clinician dashboard usage behavior, it can be determined that the clinician routinely passes over dashboards directed to the lower cost drug(s) which is indicative of the clinician considering that drug or class of drugs to be inadequate for treating the particular diseases of the patients that the clinician is treating.

As a further example, an administrator may look to various dashboards to identify protocols that are being implemented or not implemented by clinicians. While the administrator may be able to obtain the analytics associated with the protocols and thereby identify which protocols are being followed and which are not, the administrator is not given any reasoning as to why certain protocols are not being followed. However, if one could correlate clinician behaviors with regard to other dashboards with the analytics presented to the administrator via his or her preferred dashboards, then a more useful insight is given to the administrator and recommendations may be generated for modifying existing dashboards or creating new dashboards that will minimize unwanted behaviors. For example, if it is determined that clinicians are routinely failing to view certain dashboards that are tied to the protocols that are not being implemented satisfactorily, then this usage behavior may be indicative of the reason why the protocol is not being implemented, i.e. the clinicians are not aware of the protocol because they do not find the dashboard that informs them of the protocol to be particularly useful to their duties of treating patients. In such a case, recommendations may be generated to recommend modification of other dashboards that the clinicians do use, as determined from usage behavior information, to include portions of the dashboard that facilitate compliance with the overlooked or non-implemented protocol.

The cognitive system 710 may utilize data obtained from the predictive analytics logic 357 that analyzes the behavior of users and predicts the information needs of those users in the manner described with regard to one or more of the illustrative embodiments above, to thereby represent a user dashboard behavior. Moreover, in some illustrative embodiments, the raw data and/or analytics results data generated by analytics engines 330 may be utilized by the cognitive system 710 as well to evaluate user dashboard behavior. This user dashboard behavior may be evaluated for a plurality of different types of users and/or user groups. That is, information in the user registry 356 may be utilized to correlate the identified user dashboard behaviors with particular users who may further have designated user groups with which they belong, e.g., clinician, administrator, laboratory technician, government agency representative, etc. These user groups are defined according to one or more user characteristics with all members of the particular user group having the specific one or more user characteristics, or user type, that define the group, e.g., all members of the “clinicians” group would have a job classification of “clinician”. Thus, user dashboard behaviors for different users and/or user groups may be identified using the mechanisms previously described above.

The cognitive system 710 provides further cognitive analysis logic for analyzing these user dashboard behaviors and correlating such user dashboard behaviors so as to generate additional insights into the behaviors and generate additional dashboards and/or dashboard recommendations, both for the users themselves and for administrators who may be looking to modify or improve upon the dashboard offerings that are provided via the predefined dashboards 345. The cognitive system 710 may utilize behavior pattern reasoning logic 712 that is trained to analyze attributes of dashboards and attributes of patterns of user dashboard behaviors, i.e. user dashboard behavior pattern data, to identify corresponding candidate reasons for such user dashboard behaviors. The behavior pattern reasoning logic 712 may further be trained to recognize correlations between candidate reasons for user dashboard behaviors amongst different users and/or user groups. The behavior pattern reasoning logic 712 of the cognitive system 710 may operate based on additional information resources, such as the dashboard knowledge base 720, organizational database 730, user registry 356, attributes of the predefined dashboards specified in the predefined dashboards database 345, and dashboard usage data 355 to perform such behavior pattern reasoning operations.

The behavior pattern reasoning logic 712 evaluates the various candidate reasons for user dashboard behaviors, the user dashboard behaviors being identified based on analysis performed by the predictive analytics logic 357 for example, and/or analysis of the raw data or analytics results data obtained from the analytics engines, to generate confidence scores associated with such candidate reasons. Similarly, the behavior pattern reasoning logic 712 further evaluates candidate correlations between user dashboard behaviors across a plurality of different users and/or user groups, and generates confidence scores for these candidate correlations. Based on the confidence scoring of these candidate reasons and candidate correlations, the top ranking confidence score candidates may be selected for use in generating modified or new dashboards and/or recommendations to be output to administrators via administrator console(s) 380 and/or users via client systems 370. Based on the selected candidate reasons and correlations, the DUTAR system may generate dashboard cross user recommendations that are sent to administrator console(s) 380 and/or dashboard recommendations, which may be provided as part of a dashboard output, sent to client system(s) 370.

It should be appreciated that the cognitive system 710 comprises algorithms executed by computing hardware to approximate human thinking patterns in order to perform reasoning and analysis. One or more of these algorithms executed by computing hardware may be implemented as part of the behavior pattern reasoning logic 712. As an overview, a cognitive system, such as cognitive system 710, is a specialized computer system, or set of computer systems, configured with hardware and/or software logic (in combination with hardware logic upon which the software executes) to emulate human cognitive functions. These cognitive systems perform operations that are not generic to computing devices and are not simply general computer functions of data input, processing, and data output in general, but instead apply human-like characteristics to conveying and manipulating ideas which, when combined with the inherent strengths of digital computing, can solve problems with high accuracy and resilience on a large scale. A cognitive system performs one or more computer-implemented cognitive operations that approximate a human thought process as well as enable people and machines to interact in a more natural manner so as to extend and magnify human expertise and cognition.

A cognitive system comprises artificial intelligence logic, such as natural language processing (NLP) based logic, for example, and machine learning logic, which may be provided as specialized hardware, software executed on hardware, or any combination of specialized hardware and software executed on hardware. The logic of the cognitive system implements the cognitive operation(s), examples of which include, but are not limited to, question answering, identification of related concepts within different portions of content in a corpus, intelligent search algorithms, such as Internet web page searches, for example, medical diagnostic and treatment recommendations, and other types of recommendation generation, e.g., items of interest to a particular user, potential new contact recommendations, or the like. In the context of the present description, the cognitive system 710 performs cognitive operations for analyzing the reasons behind user dashboard behavior patterns and the correlations between user dashboard behavior patterns for different types of users or different user groups.

IBM Watson™ is an example of one such cognitive system which can process human readable language and identify inferences between text passages with human-like high accuracy at speeds far faster than human beings and on a larger scale. In general, such cognitive systems are able to perform the following functions:

-   -   Navigate the complexities of human language and understanding     -   Ingest and process vast amounts of structured and unstructured         data     -   Generate and evaluate hypotheses     -   Weigh and evaluate responses that are based only on relevant         evidence     -   Provide situation-specific advice, insights, and guidance     -   Improve knowledge and learn with each iteration and interaction         through machine learning processes     -   Enable decision making at the point of impact (contextual         guidance)     -   Scale in proportion to the task     -   Extend and magnify human expertise and cognition     -   Identify resonating, human-like attributes and traits from         natural language     -   Deduce various language specific or agnostic attributes from         natural language     -   High degree of relevant recollection from data points (images,         text, voice) (memorization and recall)     -   Predict and sense with situational awareness that mimics human         cognition based on experiences     -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for processing requests via one or more request processing pipelines in which a large number of various algorithms are executed on input data to evaluate those data with regard to various criteria specific to the algorithms and generate results which may be utilized by other algorithms in the pipeline to achieve a cognitive operation. The request pipeline or system, as may be implemented, for example, in the behavior pattern reasoning logic 712 of the cognitive system 710, is an artificial intelligence application executing on data processing hardware that processes requests pertaining to a given subject-matter domain. The request processing pipeline receives inputs from various sources including input over a network, a corpus of electronic documents or other data, data from a content creator, information from one or more content users, and other such inputs from other possible sources of input. Data storage devices store the corpus of data, such as dashboard knowledge base 720, organizational database 730, user registry 356, predefined dashboards 345, dashboard usage data 355, and the like, upon which the cognitive system operates to perform cognitive operations, such as user dashboard behavior analysis across different types of users and/or user groups, user dashboard behavior reasoning, and the like. The corpus of data provides knowledge to the cognitive system 710 over which the cognitive system reasons by evaluating the data using the various algorithms implemented by the cognitive system.

In some illustrative embodiments, content users, or automated mechanisms of the predictive analytics logic 357, may input questions or information requests to the cognitive system 710 which implements the request processing pipeline, such as in behavior pattern reasoning logic 712. The request processing pipeline then answers the input questions or responds to the information requests using the content in the corpus of data by evaluating documents, sections of documents, portions of data in the corpus, or the like. When a process evaluates a given section of a document for semantic content, the process can use a variety of conventions to query such document from the request processing pipeline, e.g., sending the query to the request processing pipeline as a well-formed question or information request which is then interpreted by the request processing pipeline and a response is provided containing one or more answers to the question or portions of information determined to satisfy the information request. Semantic content is content based on the relation between signifiers, such as words, phrases, signs, and symbols, and what they stand for, their denotation, or connotation. In other words, semantic content is content that interprets an expression, such as by using Natural Language Processing.

Assuming that the request processing pipeline operates on natural language questions posed by users, such as a user of an administrator console 380 or client system 370, the request processing pipeline receives an input question, parses the question to extract the major features of the question, uses the extracted features to formulate queries, and then applies those queries to the corpus of data. Based on the application of the queries to the corpus of data, the request processing pipeline generates a set of hypotheses, or candidate answers to the input question, by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question. The request processing pipeline then performs deep analysis on the language of the input question and the language used in each of the portions of the corpus of data found during the application of the queries using a variety of reasoning algorithms. There may be hundreds or even thousands of reasoning algorithms applied, each of which performs different analysis, e.g., comparisons, natural language analysis, lexical analysis, or the like, and generates a score. For example, some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm. Each resulting score is then weighted against a statistical model. The statistical model captures how well the reasoning algorithm performed at establishing the inference between two similar passages for a particular domain during the training period of the request processing pipeline. The statistical model is used to summarize a level of confidence that the request processing pipeline has regarding the evidence that the potential response, i.e. candidate answer, is inferred by the question. This process is repeated for each of the candidate answers until the request processing pipeline identifies candidate answers that surface as being significantly stronger than others and thus, generates a final answer, or ranked set of answers, for the input question.

As mentioned above, request processing pipeline mechanisms operate by accessing information from a corpus of data or information (also referred to as a corpus of content), analyzing it, and then generating answer results based on the analysis of these data. Accessing information from a corpus of data typically includes: a database query that answers questions about what is in a collection of structured records, and a search that delivers a collection of document links in response to a query against a collection of unstructured data (text, markup language, etc.). Question Answering (QA) based cognitive systems are capable of generating answers based on the corpus of data and the input question, verifying answers to a collection of questions for the corpus of data, correcting errors in digital text using a corpus of data, and selecting answers to questions from a pool of potential answers, i.e. candidate answers. The request processing pipeline generates answers for input questions using a plurality of intensive analysis mechanisms which evaluate the content to identify the most probable answers, i.e. candidate answers, for the input question. The most probable answers are output as a ranked listing of candidate answers ranked according to their relative scores or confidence measures calculated during evaluation of the candidate answers, as a single final answer having a highest ranking score or confidence measure, or which is a best match to the input question, or a combination of ranked listing and final answer.

With particular application to the depicted example illustrative embodiment in FIG. 7, as previously described above, pre-processed data stored in the backend data stores, and/or raw data depending on the particular implementation, may be provided to analytics engines for the performance of analytics operations to generate analytics results data that are stored in the backend data stores and/or provided to the user experience system 340. Moreover, the raw data and/or stored analytics results data from the backend data stores may also be provided to the user experience system 340 as shown. The user experience system 340 comprises logic for generating one or more dashboards 360 based on predefined dashboard data structures in predefined dashboards storage 345. The generated dashboards 360 are provided to the client systems 370 and user interactions with the dashboards 360 may be monitored by the dashboard usage tracking and recommendation (DUTAR) system 350, potentially with the use of client system 370 implemented agents 375.

The DUTAR system 350 listens to the user inputs to dashboards and/or other user experience systems 365 to log and/or record user inputs both before, during, and after interacting with a dashboard of interest. This information is utilized by predictive analytics logic 357 to identify user dashboard behavior patterns and predict what type of data or information the user is attempting to access and generate a recommendation for the user. Moreover, the input user dashboard behavior patterns may be accumulated with other input user dashboard behavior pattern data for the particular dashboard and stored in the dashboard usage data storage 355 for presentation of dashboard usage metrics information and/or recommendations as to how to improve dashboard offerings via administrator consoles 380.

As mentioned above, in some illustrative embodiments, recommendations may be constructed based on the user's behavior as it relates to his or her interactions with other user experience systems and, in some cases, the subject matter of the content that the user accessed via these other user experience systems. In some illustrative embodiments, similar user dashboard usage information may be combined with user dashboard behavior pattern information to generate recommendations for a user. Moreover, recommendations made for one user may also be logged in the dashboard usage data 355 and/or as part of a user's profile in the user registry 365, and used as a basis for generating recommendations for other users having similar characteristics or similar dashboard usage behavior patterns.

Thus, using the mechanisms previously described above, user dashboard behavior is determined to predict the information that the user is most likely attempting to access and thereby generate recommendations as to predefined dashboards 345 that may be able to provide the user the information sought. Moreover, recommendations may be sent to the administrators to suggest improvements to dashboard offerings.

The prediction of information sought by users may be a trigger for initiating additional cognitive operations performed by the cognitive system 710. In addition, or alternatively, explicit requests, which may be posed as natural language questions or as specific requests for information, may be received by the DUTAR system 700 from administrator console(s) 380 or client system(s) 370 to initiate cognitive operations for reasoning over the user dashboard behavior information gathered by the DUTAR system 700 and potentially stored in the dashboard usage data 355, user registry 356, and/or other portions of the corpus of information. In the case of a natural language question or request being submitted by a user via the administrator console 380 or client system 370 the cognitive system 710 may perform natural language processing on the question or request, such as by way of one or more stages of a cognitive system request processing pipeline, to determine what is being requested or what type of answer is being sought as is generally known with natural language processing based QA system or request processing pipelines.

The behavior pattern reasoning logic 712 may operate on the raw data/analytics results data, dashboard usage data 355, user registry 356 information, dashboard attribute information specified in the predefined dashboards database 345, dashboard knowledge base 720, and organizational database, to determine reasons behind the user's dashboard behavior as identified by the DUTAR system 700 and predictive analytics logic 357. For example, the dashboard knowledge base 720 may comprise rules, algorithms, and the like, that define definitions of dashboard usage conditions with resulting indications of reasons for such dashboard usage. Such rules, algorithms, and the like, correlate or map identified dashboard usage conditions, which together represent a dashboard behavior, with a particular reason for such dashboard behavior of the user.

For example, the DUTAR system 700 may identify that a user views whitepapers on drug interactions, accesses dashboards regarding drug treatments for Type II diabetes, but does not access dashboards directed to drug treatment costs per patient. These dashboard usage conditions may be reflected in rules or algorithms in the dashboard knowledge base 720 which are then implemented by the cognitive system 710, to identify reasons for the user's dashboard behavior. The dashboard knowledge base 720 may comprise many different rules, algorithms, and the like, for implementation by the behavior pattern reasoning logic 712 of the cognitive system 710 to determine candidate reasons for the particular combination of dashboard usage conditions identified by the predictive analytics logic 357 of the DUTAR system 700.

Thus, there may be multiple candidate reasons identified for a particular combination of dashboard usage conditions. Further evidence, which may be or may not be supportive of the reasoning, may be evaluated from the corpus of information to determine a confidence score associated with the candidate reason. For example, if a reason for the user dashboard behavior is determined to be that the user is concerned about drug interactions for treating Type II diabetes, then further evidence that this reason may be the correct reason for the user dashboard behavior may be obtained from looking at the user registry 356 to determine the type of user, e.g., if a user is a clinician, then this is more evidence that the reason is correct than if the user is a hospital administrator, the organizational database 730 which may indicate a position of the user within an organization, e.g., employed by the emergency room of a hospital is more evidence that the reason is correct than if the user is employed by a research arm of the hospital, the predefined dashboard attributes from database 345 and dashboard usage data in the data structure 355 may indicate the types of dashboards actually used by the user which may indicate dashboards that provide drug treatment information which is further indicative of the reason being a correct reason than dashboards concerned with costs of the organization, outbreaks in geographical regions, etc.

A ranked listing of candidate reasons for a user's dashboard behavior may then be generated based on the confidence scores associated with the candidate reasons. A top ranked candidate reason may then be selected as the probable reason for the user's dashboard behavior. Thus, for example, the behavior pattern reasoning logic 712 of the cognitive system 710 may determine that the reason the user is accessing certain information from various user experience systems 340, dashboards, and performing instant messaging and searches via search engines, is because the user is attempting to determine drug interactions between drugs used to treat patients for Type II diabetes. This is different from merely determining what dashboard provides information that the user may be interested in.

In addition, the dashboard knowledge base 720 may provide rules, algorithms, and the like, that are implemented in the behavior pattern reasoning logic 712 of the cognitive system 710 to perform cross-user dashboard behavior correlation and analysis. That is, these cross-user dashboard behavior correlation and analysis rules, algorithms, and the like, analyze the user dashboard behavior patterns across multiple different types of users and/or user groups, as may be indicated from indication of user types and/or associated user groups for users in user profiles in the user registry 365. For example, cross-user dashboard behavior correlation and analysis rules, algorithms, and the like, may specify that particular user types that are determined to be seeking certain information from dashboards for particular identified reasons may be correlated with other user types that are determined to be seeking other information from dashboards, but where the information has some measure of commonality or where the reasons for the users' dashboard behavior have particular conceptual correlations, as may be specified via an ontological data structure or the like that may be part of the dashboard knowledge base 720. For example, it may be determined that both clinicians and hospital administrators are interested in drug treatments for Type II diabetes even though the reasons for the clinician may be directed to treatment of patients while the reasons for the hospital administrators may be from a cost perspective. Moreover, it may be determined that reasons of drug treatment effectiveness and drug treatment costs are related concepts via an ontological data structure such that the reasons for user dashboard behaviors may be correlated. Such correlations may be identified between user dashboard behaviors across different types of users and/or user groups to thereby identify intersection points in the dashboard behaviors of different types of users and/or user groups. Similar to the evaluation of candidate reasons for user dashboard behavior, the behavior pattern reasoning logic 712 may also generate candidate correlations between user dashboard behavior across multiple user types and/or user groups, e.g., a plurality of pairings or user types and/or user groups may be generated and each pairing may be evaluated for correlations, i.e. intersection points in the dashboard usage patterns of the different types of users, e.g., both types of users access information about a particular class of drugs (although for different reasons), with each of the correlations being further evaluated for evidential support to generate candidate correlation confidence scores. A ranked listing of candidate correlations may then be generated based on the candidate correlation confidence scores with a top ranking candidate correlation being selected for use in generating a dashboard recommendation and/or dashboard offering modification recommendations.

The DUTAR system 700 maps the selected reason for user dashboard behavior and/or selected cross-user correlation to appropriate recommendations for dashboards and/or dashboard offering modifications. For example, the mapping performed by the DUTAR system 700 may provide a recommendation to the user via a client system 370 to recommend another dashboard that the user may access to obtain information to provide decision support based on the determined reason for the user's dashboard behavior. As another example, the DUTAR system 700 may provide a recommendation to an administrator via the administrator console 380 as to a new dashboard, combination of dashboards, or portion of a dashboard that may be added to dashboard offerings in the predefined dashboards 345 that provide cross-user usability by providing information that satisfies the reasons for user dashboard behavior across multiple types of users and/or user groups.

Thus, for example, in one illustrative embodiment, the DUTAR system 700 may observe and determine that a particular user spends time reading whitepapers, via another user experience system 365, related to prescription drug costs for the drug Embrel® and interacting with a dashboard designed to show how much the user's organization is currently spending on prescription medication. Thus, the predictive analytics logic 357 may determine that the user is attempting to access costs for drug treatments for rheumatoid arthritis and, based on the user dashboard behavior, has not seemed to find the information sought in the dashboards accessed. As a result, an automatic generation of a request may be performed to request that the cognitive system 710, which may be the IBM Watson™ cognitive system, for example, evaluate other types of users or user groups' dashboard behavior with regard to dashboards that provide information directed to costs for drug treatments for rheumatoid arthritis. Other users', or user groups', dashboard behavior that intersects with the current user's dashboard behavior may be identified to identify correlations, or intersection points, between user dashboard behavior data. For example, the current user may be determined to be a hospital administrator from user profile information in the user registry 356. The cognitive system 710 may operate to identify other users, e.g., clinician users, that also look to similar dashboards that provide information regarding rheumatoid arthritis drug treatments, but not necessarily for organizational costs purposes. The organization database 730 may comprise policy information for the organization that states that the organization wishes to reduce costs to their patients and to the organization with regard to rheumatoid arthritis treatment and thus, taking this information into consideration, it may be determined that it may be beneficial to recommend to the clinician a dashboard, or portion of a dashboard, that reflects costs of drug treatments for rheumatoid arthritis along with dashboards, or portions of dashboards, that present information about the effectiveness of drug treatments for patients of different demographics.

Moreover, a dashboard cross user recommendation may be sent to the administrator console 380 indicating a recommended improvement to the dashboard offerings which may include a new or modified dashboard in the predefined dashboards 345 that accommodates both the administrator's concerns regarding costs to the organization of rheumatoid arthritis treatments and the clinician's concerns regarding effectiveness of such treatments. As a result, both users are given greater insight into the underlying analytics to support decision making from other user viewpoints, i.e. the administrator is able to determine that the clinician is choosing drug treatments based on effectiveness and the clinician is able to see the costs of such drug treatment choices to the patient and/or organization. As a result, more informed decisions may be made on both parts whereas in other situations without this cross user recommendation mechanism, each individual user would see the analytics information that is specific to his individual needs and may not be informed of the viewpoint of the other user.

FIG. 8 is a flowchart outlining an example operation for performing cross-user dashboard behavior analysis and dashboard recommendations in accordance with one illustrative embodiment. As shown in FIG. 8, the operation starts by receiving a request to perform cross-user dashboard behavior correlation and recommendation (step 810). This request may be an automatically generated request generated based on a predicted information need for a user by predictive analytics logic, for example. This request may also be a specific request or natural language question posed by a user via a user computing device, such as an administrator console or client system.

Corresponding tracked user dashboard behavior data are received (step 820) and cognitive analysis of the tracked user dashboard behavior is performed to identify the reasoning for the user dashboard behavior (step 830). Such reasoning may involve cognitive processing of a corpus of information regarding the user, the dashboards, the user's interaction with the dashboards, the user's interactions with other user experience systems, as well as organizational information, dashboard knowledge base information, and the like. The cognitive processing may include implementing rules, algorithms, or the like, that correlate dashboard usage conditions with reasons for such, evaluating evidential support for these candidate reasons, generating confidence scores for these candidate reasons, and selection of a top ranking candidate reason as the reason for the user dashboard behavior.

Cross-user correlation analysis is then performed based on the identified reason for the user dashboard behavior (step 840). The cross-user correlation analysis may further comprise cognitive operations similar to that above being applied across multiple user types and/or user groups to identify candidate correlations, evaluate evidential support for these candidate correlations, rank the candidate correlations based on their corresponding confidence scores, and then select a top ranking candidate correlation. Thereafter, a cross-user dashboard recommendation is generated based on the results of the cross-user correlation analysis (step 850). The cross-user dashboard recommendation is output to the user/administrator for recommending dashboards/modification of dashboard offerings (step 860). The operation then terminates.

As noted above, it should be appreciated that the illustrative embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment containing both hardware and software elements. In one example embodiment, the mechanisms of the illustrative embodiments are implemented in software or program code, which includes but is not limited to firmware, resident software, microcode, etc.

A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a communication bus, such as a system bus, for example. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. The memory may be of various types including, but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory, solid state memory, and the like.

Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening wired or wireless I/O interfaces and/or controllers, or the like. I/O devices may take many different forms other than conventional keyboards, displays, pointing devices, and the like, such as communication devices coupled through wired or wireless connections including, but not limited to, smart phones, tablet computers, touch screen devices, voice recognition devices, and the like. Any known or later developed I/O device is intended to be within the scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters for wired communications. Wireless communication based network adapters may also be utilized including, but not limited to, 802.11 a/b/g/n wireless communication adapters, BLUETOOTH™ wireless adapters, and the like. Any known or later developed network adapters are intended to be within the spirit and scope of the present invention.

The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The embodiment was chosen and described in order to best explain the principles of the invention, the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 

What is claimed is:
 1. A method, in a data processing system comprising at least one memory and at least one processor, wherein the at least one memory comprises instructions that are executed by the at least one processor to configure the at least one processor to implement the method, the method comprising: presenting, by the data processing system, one or more dashboard interfaces to a user via a client computing device; tracking, by the data processing system, user inputs to one or more user experience computing systems, different from the one or more dashboard interfaces, of the client computing device, at least during and after presentation of each of the one or more dashboard interfaces to the user via the client computing device to generate user dashboard behavior pattern data; performing, by behavior pattern reasoning logic of a cognitive computing system, cognitive analysis of the user dashboard behavior pattern data to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data, wherein performing cognitive analysis of the user dashboard behavior pattern data comprises executing a predefined set of computer executed first rules that map each dashboard behavior pattern to one or more corresponding candidate reasons for the dashboard behavior pattern and rank the one or more corresponding candidate reasons based on a cognitive computing processing of evidence data in support of each candidate reason in the one or more corresponding candidate reasons; performing, by the behavior pattern reasoning logic of the cognitive computing system, cross-user correlation analysis operations based on the user dashboard behavior pattern data and dashboard behavior pattern data of one or more other users of a different user type to identify at least one intersection point where the user dashboard behavior of the user intersects with user dashboard behaviors of the one or more other users, wherein performing cross-user correlation analysis operations comprises executing a predefined set of computer executed second rules that correlate characteristics of dashboards accessed by the user and the one or more other users to identify the at least one intersection point; and outputting, by the data processing system, a recommendation output that recommends at least one of a particular dashboard interface to access or a modification to the one or more dashboard interfaces to be performed, to the user via the client computing device, based on the identification of the intersection points and the determined reason for the user dashboard behavior.
 2. The method of claim 1, wherein tracking user inputs to the one or more user experience computing systems, different from the one or more dashboard interfaces, of the client computing device at least during and after presentation of each of the one or more dashboard interfaces to the user via the client computing device to generate user dashboard behavior pattern data further comprises applying predictive analytics, by predictive analytics logic of the data processing system, to the tracked user inputs to predict information needs of the user.
 3. The method of claim 1, wherein the behavior pattern reasoning logic is trained to analyze attributes of user dashboard behavior pattern data, to identify corresponding candidate reasons for user dashboard behavior represented by the user dashboard behavior pattern data and to identify correlations between candidate reasons for user dashboard behavior across a plurality of different user types.
 4. The method of claim 1, wherein performing, by behavior pattern reasoning logic of a cognitive computing system, cognitive analysis of the user dashboard behavior pattern data to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data comprises: generating a set of candidate reasons for the user dashboard behavior based on application, by the cognitive computing system, of one or more trained algorithms to the user dashboard behavior data to correlate attributes of the user dashboard behavior data to candidate reasons for the user dashboard behavior; generating, for each of the candidate reasons in the set of candidate reasons, a confidence score associated with the candidate reason based on cognitive analysis of evidence data corresponding to the candidate reason; and selecting a reason for user dashboard behavior from the set of candidate reasons based on the confidence scores associated with each of the candidate reasons.
 5. The method of claim 1, wherein performing cross-user correlation analysis operations based on the user dashboard behavior pattern data and dashboard behavior pattern data of one or more other users of a different user type to identify at least one intersection point where the user dashboard behavior of the user intersects with user dashboard behaviors of the one or more other users comprises: generating a set of candidate correlations between the user dashboard behavior and user dashboard behaviors of the one or more other users based on application, by the cognitive computing system, of one or more trained algorithms to the user dashboard behavior data and user dashboard behavior data of the one or more other users to map attributes of the user dashboard behavior data with attributes of the user dashboard behavior data of the one or more other users that represent candidate correlations; generating, for each of the candidate correlations in the set of candidate correlations, a confidence score associated with the candidate correlation based on cognitive analysis of evidence data corresponding to the candidate correlation; and selecting a correlation, specifying the at least one intersection point, from the set of candidate correlations based on the confidence scores associated with each of the candidate correlations.
 6. The method of claim 1, wherein performing cross-user correlation analysis comprises identifying, by the behavior pattern reasoning logic of the cognitive computing system, portions of one or more first dashboard interfaces interacted with by the user and portions of one or more other second dashboard interfaces interacted with by at least one of the one or more other users, that present the same information.
 7. The method of claim 1, wherein the recommendation output is a cross-user recommendation for modifying an existing dashboard interface, or generating a new dashboard interface, to comprise at least one portion of the dashboard interface directed to the at least one intersection point.
 8. The method of claim 1, wherein performing the cognitive analysis of the user dashboard behavior pattern data, performing the cross-user correlation analysis operations, and outputting the recommendation output are performed in response to a request to perform cross-user cognitive analysis of user dashboard behavior patterns.
 9. The method of claim 8, wherein the request is automatically generated by the data processing system based on results of predictive analytics logic of the data processing system indicating that cross-user cognitive analysis is to be performed.
 10. The method of claim 1, wherein: tracking user inputs to one or more user experience computing systems comprises extracting key terms or key phrases entered by the user into a text input field of at least one subsequent user experience system after presentation of at least one of the dashboard interfaces in the one or more dashboard interfaces; performing cognitive analysis of the user dashboard behavior pattern data comprises searching metadata associated with a plurality of other second dashboard interfaces based on the extracted key terms or key phrases to identify metadata having terms or phrases matching the extracted key terms or key phrases; and outputting the recommendation output comprises selecting, based on results of searching the metadata associated with the one or more other second dashboard interfaces, at least one second dashboard interface from the plurality of other second dashboard interfaces that has metadata having terms or phrases matching the extracted key terms or key phrases, for inclusion in the recommendation output, wherein the metadata associated with each second dashboard interface describe content of one or more portions of a corresponding second dashboard interface.
 11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to be specifically configured to implement a cognitive computing system, and to: present one or more dashboard interfaces to a user via a client computing device; track user inputs to one or more user experience computing systems, different from the one or more dashboard interfaces, of the client computing device at least during and after presentation of each of the one or more dashboard interfaces to the user via the client computing device to generate user dashboard behavior pattern data; perform, by behavior pattern reasoning logic of the cognitive computing system, cognitive analysis of the user dashboard behavior pattern data to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data, wherein performing cognitive analysis of the user dashboard behavior pattern data comprises executing a predefined set of computer executed first rules that map each dashboard behavior pattern to one or more corresponding candidate reasons for the dashboard behavior pattern and rank the one or more corresponding candidate reasons based on a cognitive computing processing of evidence data in support of each candidate reason in the one or more corresponding candidate reasons; perform, by the behavior pattern reasoning logic of the cognitive computing system, cross-user correlation analysis operations based on the user dashboard behavior pattern data and dashboard behavior pattern data of one or more other users of a different user type to identify at least one intersection point where the user dashboard behavior of the user intersects with user dashboard behaviors of the one or more other users, wherein performing cross-user correlation analysis operations comprises executing a predefined set of computer executed second rules that correlate characteristics of dashboards accessed by the user and the one or more other users to identify the at least one intersection point; and output a recommendation output that recommends at least one of a particular dashboard interface to access or a modification to the one or more dashboard interfaces to be performed, to the user via the client computing device, based on the identification of the intersection points and the determined reason for the user dashboard behavior.
 12. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to track user inputs to the one or more user experience computing systems, different from the one or more dashboard interfaces, of the client computing device at least during and after presentation of each of the one or more dashboard interfaces to the user via the client computing device to generate user dashboard behavior pattern data further at least by applying predictive analytics, by predictive analytics logic of the data processing system, to the tracked user inputs to predict information needs of the user.
 13. The computer program product of claim 11, wherein the behavior pattern reasoning logic is trained to analyze attributes of user dashboard behavior pattern data, to identify corresponding candidate reasons for user dashboard behavior represented by the user dashboard behavior pattern data and to identify correlations between candidate reasons for user dashboard behavior across a plurality of different user types.
 14. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to perform cognitive analysis of the user dashboard behavior pattern data to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data at least by: generating a set of candidate reasons for the user dashboard behavior based on application, by the cognitive computing system, of one or more trained algorithms to the user dashboard behavior data to correlate attributes of the user dashboard behavior data to candidate reasons for the user dashboard behavior; generating, for each of the candidate reasons in the set of candidate reasons, a confidence score associated with the candidate reason based on cognitive analysis of evidence data corresponding to the candidate reason; and selecting a reason for user dashboard behavior from the set of candidate reasons based on the confidence scores associated with each of the candidate reasons.
 15. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to perform cross-user correlation analysis operations based on the user dashboard behavior pattern data and dashboard behavior pattern data of one or more other users of a different user type to identify at least one intersection point where the user dashboard behavior of the user intersects with user dashboard behaviors of the one or more other users at least by: generating a set of candidate correlations between the user dashboard behavior and user dashboard behaviors of the one or more other users based on application, by the cognitive computing system, of one or more trained algorithms to the user dashboard behavior data and user dashboard behavior data of the one or more other users to map attributes of the user dashboard behavior data with attributes of the user dashboard behavior data of the one or more other users that represent candidate correlations; generating, for each of the candidate correlations in the set of candidate correlations, a confidence score associated with the candidate correlation based on cognitive analysis of evidence data corresponding to the candidate correlation; and selecting a correlation, specifying the at least one intersection point, from the set of candidate correlations based on the confidence scores associated with each of the candidate correlations.
 16. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to perform cross-user correlation analysis at least by identifying, by the behavior pattern reasoning logic of the cognitive computing system, portions of one or more first dashboard interfaces interacted with by the user and portions of one or more other second dashboard interfaces interacted with by at least one of the one or more other users, that present the same information.
 17. The computer program product of claim 11, wherein the recommendation output is a cross-user recommendation for modifying an existing dashboard interface, or generating a new dashboard interface, to comprise at least one portion of the dashboard interface directed to the at least one intersection point.
 18. The computer program product of claim 11, wherein performing the cognitive analysis of the user dashboard behavior pattern data, performing the cross-user correlation analysis operations, and outputting the recommendation output are performed in response to an automatically generated request to perform cross-user cognitive analysis of user dashboard behavior patterns, which is automatically generated by the data processing system based on results of predictive analytics logic of the data processing system indicating that cross-user cognitive analysis is to be performed.
 19. The computer program product of claim 11, wherein: tracking user inputs to one or more user experience computing systems comprises extracting key terms or key phrases entered by the user into a text input field of at least one subsequent user experience system after presentation of at least one of the dashboard interfaces in the one or more dashboard interfaces; performing cognitive analysis of the user dashboard behavior pattern data comprises searching metadata associated with a plurality of other second dashboard interfaces based on the extracted key terms or key phrases to identify metadata having terms or phrases matching the extracted key terms or key phrases; and outputting the recommendation output comprises selecting, based on results of searching the metadata associated with the one or more other second dashboard interfaces, at least one second dashboard interface from the plurality of other second dashboard interfaces that has metadata having terms or phrases matching the extracted key terms or key phrases, for inclusion in the recommendation output, wherein the metadata associated with each second dashboard interface describe content of one or more portions of a corresponding second dashboard interface.
 20. An apparatus comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to be specifically configured to implement a cognitive computing system and to: present one or more dashboard interfaces to a user via a client computing device; track user inputs to one or more user experience computing systems, different from the one or more dashboard interfaces, of the client computing device at least during and after presentation of each of the one or more dashboard interfaces to the user via the client computing device to generate user dashboard behavior pattern data; perform, by behavior pattern reasoning logic of the cognitive computing system, cognitive analysis of the user dashboard behavior pattern data to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data, wherein performing cognitive analysis of the user dashboard behavior pattern data comprises executing a predefined set of computer executed first rules that map each dashboard behavior pattern to one or more corresponding candidate reasons for the dashboard behavior pattern and rank the one or more corresponding candidate reasons based on a cognitive computing processing of evidence data in support of each candidate reason in the one or more corresponding candidate reasons; perform, by the behavior pattern reasoning logic of the cognitive computing system, cross-user correlation analysis operations based on the user dashboard behavior pattern data and dashboard behavior pattern data of one or more other users of a different user type to identify at least one intersection point where the user dashboard behavior of the user intersects with user dashboard behaviors of the one or more other users, wherein performing cross-user correlation analysis operations comprises executing a predefined set of computer executed second rules that correlate characteristics of dashboards accessed by the user and the one or more other users to identify the at least one intersection point; and output a recommendation output that recommends at least one of a particular dashboard interface to access or a modification to the one or more dashboard interfaces to be performed, to the user via the client computing device, based on the identification of the intersection points and the determined reason for the user dashboard behavior. 